diff --git a/backend/app/indicators/pipeline.py b/backend/app/indicators/pipeline.py
index 143e32a..41cabb5 100644
--- a/backend/app/indicators/pipeline.py
+++ b/backend/app/indicators/pipeline.py
@@ -16,6 +16,9 @@
 from __future__ import annotations
 
 import logging
+import shutil
+import time
+import uuid
 from collections.abc import Callable
 from pathlib import Path
 
@@ -1287,6 +1290,105 @@ def attach_deviation_columns_today(
     )
 
 
+# ================================================================
+# 全量重建流式暂存 + 自适应批次 (#208/#174)
+#
+# 旧全量模式把所有批次结果累积在内存 date_buffers 直到统一写盘:
+# 延长历史后 (5年 × 5500 只 ≈ 800 万行) 「全表驻留 + 单批宽表」双双
+# 超出小内存机器上限, 重建必然 OOM。现改为:
+#   - 每批结果立即写暂存文件 (enriched 树外的隐藏目录 —— polars/duckdb
+#     的 **/*.parquet glob 均会匹配点目录, 树内暂存会被业务读取扫到),
+#     最后按日期分块流式合并、逐分区原子替换;
+#   - 批次大小按单批目标行数自适应收缩 (指标/信号全部 over("symbol")
+#     分组, symbol 级分批不改变计算结果, 只约束单批宽表峰值)。
+# 任一时刻峰值内存 = 单批计算 + 单个日期块合并, 与总历史长度无关。
+# ================================================================
+
+_STAGING_ROOT = Path(".staging") / "enriched_rebuild"
+_STALE_STAGING_MAX_AGE_S = 24 * 3600
+_RAM_LARGE_BYTES = 8 * 1024 ** 3      # ≥8GB 视为内存充裕, 批次保持用户设置
+_BATCH_TARGET_ROWS = 150_000          # 小内存单批目标行数 (宽表 ~60-80MB)
+_BATCH_MIN_SYMBOLS = 50
+_MERGE_DATE_CHUNKS = 15               # 最终合并按日期切 15 块流式执行
+
+_ram_bytes_cache: int | None | bool = False  # False = 未探测
+
+
+def _total_ram_bytes() -> int | None:
+    global _ram_bytes_cache
+    if _ram_bytes_cache is False:
+        try:
+            import psutil
+            _ram_bytes_cache = psutil.virtual_memory().total
+        except Exception:
+            _ram_bytes_cache = None
+    return _ram_bytes_cache  # type: ignore[return-value]
+
+
+def _adaptive_sym_batch(default_batch: int, rows_per_symbol: int) -> int:
+    """小内存机器按单批目标行数收缩批次; 大内存机器保持原值 (#208)。"""
+    if (_total_ram_bytes() or 0) >= _RAM_LARGE_BYTES:
+        return default_batch
+    return max(
+        _BATCH_MIN_SYMBOLS,
+        min(default_batch, _BATCH_TARGET_ROWS // max(rows_per_symbol, 1)),
+    )
+
+
+def _sweep_stale_staging(data_dir: Path) -> None:
+    """清理崩溃/取消运行残留的暂存目录 (按 mtime 判定, 不碰活跃目录)。"""
+    root = data_dir / _STAGING_ROOT
+    if not root.exists():
+        return
+    cutoff = time.time() - _STALE_STAGING_MAX_AGE_S
+    for run_dir in root.iterdir():
+        try:
+            if run_dir.is_dir() and run_dir.stat().st_mtime < cutoff:
+                shutil.rmtree(run_dir, ignore_errors=True)
+        except OSError:
+            pass
+
+
+def compute_enriched_history_window(
+    df_hist: pl.DataFrame,
+    data_dir: Path,
+    instruments: pl.DataFrame | None = None,
+    historical_shares: pl.DataFrame | None = None,
+    sym_batch: int | None = None,
+) -> pl.DataFrame:
+    """按 symbol 分批执行历史窗口计算: 指标 → 偏离列 → 信号 → 涨跌停。
+
+    与整帧顺序执行完全等价 (各步骤均 over("symbol") 分组), 分批只约束
+    峰值内存: repository._refresh_enriched 的 300 天窗口在 5500 只、
+    210 交易日下整帧宽表 ~1.2GB, 小内存机器启动即 OOM (#208)。
+    sym_batch 显式传入时跳过自适应 (测试用)。
+    """
+    if df_hist.is_empty() or "symbol" not in df_hist.columns:
+        return df_hist
+    symbols = df_hist["symbol"].unique().sort().to_list()
+    if sym_batch is None:
+        rows_per_sym = max(1, df_hist.height // max(len(symbols), 1))
+        sym_batch = _adaptive_sym_batch(2000, rows_per_sym)
+    parts: list[pl.DataFrame] = []
+    for bs in range(0, len(symbols), sym_batch):
+        batch = symbols[bs:bs + sym_batch]
+        part = df_hist.filter(pl.col("symbol").is_in(batch)).sort(["symbol", "date"])
+        part = compute_indicators(part)
+        part = attach_deviation_columns(part, data_dir)
+        part = compute_signals(part)
+        if instruments is not None and not instruments.is_empty():
+            inst_batch = instruments.filter(pl.col("symbol").is_in(batch))
+            shares_batch = (
+                historical_shares.filter(pl.col("symbol").is_in(batch))
+                if historical_shares is not None and not historical_shares.is_empty()
+                else historical_shares
+            )
+            part = compute_limit_signals(part, inst_batch, historical_shares=shares_batch)
+        parts.append(part)
+    out = parts[0] if len(parts) == 1 else pl.concat(parts, how="diagonal_relaxed")
+    return out.sort(["symbol", "date"])
+
+
 def run_pipeline(data_dir: Path | None = None,
                  symbols: list[str] | None = None,
                  new_dates_only: bool = False,
@@ -1460,9 +1562,10 @@ def run_pipeline(data_dir: Path | None = None,
 
     import gc
 
-    # ── 按 symbol 分批处理: 每只股只有 ~244 行, 无冗余计算 ──
-    # 先获取全部 symbol 列表
-    lf_all = scan_daily_parquet(daily_glob, cast_options=_cast)
+    # ── 按 symbol 分批处理: 指标全部 over("symbol") 分组, 分批不改变结果 ──
+    # 文件列表只收集一次, 批间复用 (避免每批重新展开 glob)
+    daily_files = sorted(str(p) for p in daily_dir.rglob("*.parquet"))
+    lf_all = scan_daily_parquet(daily_files, cast_options=_cast)
     if symbols:
         sym_set = set(symbols)
         lf_all = lf_all.filter(pl.col("symbol").is_in(list(sym_set)))
@@ -1476,7 +1579,6 @@ def run_pipeline(data_dir: Path | None = None,
         return 0
 
     total_syms = len(all_symbols)
-    logger.info("全量计算: %d 只标的, 按 symbol 分批 [%s]", total_syms, mode)
 
     if not factors.is_empty() and symbols:
         factors = factors.filter(pl.col("symbol").is_in(list(sym_set)))
@@ -1486,106 +1588,139 @@ def run_pipeline(data_dir: Path | None = None,
         inst_use = instruments.filter(pl.col("symbol").is_in(list(sym_set)))
 
     from app.services import preferences as prefs_mod
-    SYM_BATCH = prefs_mod.get_enriched_batch_size()  # 每批 N 只 × ~244 天, 可在设置中调整
+    # 自适应批次 (#208): 单批体积按目标行数恒定, 与总历史长度解耦;
+    # 小内存机器自动收缩, 大内存机器保持用户设置
+    total_rows = lf_all.select(pl.len()).collect(streaming=True).item()
+    rows_per_sym = max(1, -(-int(total_rows) // total_syms))
+    SYM_BATCH = _adaptive_sym_batch(prefs_mod.get_enriched_batch_size(), rows_per_sym)
     total_batches = (total_syms + SYM_BATCH - 1) // SYM_BATCH
+    logger.info("全量计算: %d 只标的 (%d 行, ~%d 行/只), symbol 分批 %d 只/批, %d 批 [%s]",
+                total_syms, total_rows, rows_per_sym, SYM_BATCH, total_batches, mode)
 
-    # 全量模式: 收集所有批次结果, 最后按日期分区覆盖写入
-    from collections import defaultdict
-    date_buffers: dict[str, list[pl.DataFrame]] = defaultdict(list)
+    # 全量模式: 流式暂存发布 (#208) —— 每批落盘暂存文件, 不再内存累积;
+    # 暂存目录在 enriched 树外, 不会被任何 **/*.parquet 业务 glob 扫到
+    staging_dir: Path | None = None
+    staging_files: list[str] = []
+    if not symbols:
+        _sweep_stale_staging(d)
+        staging_dir = d / _STAGING_ROOT / uuid.uuid4().hex
+        staging_dir.mkdir(parents=True, exist_ok=True)
 
-    for batch_start in range(0, total_syms, SYM_BATCH):
-        batch_end = min(batch_start + SYM_BATCH, total_syms)
-        batch_syms = all_symbols[batch_start:batch_end]
+    try:
+        for batch_start in range(0, total_syms, SYM_BATCH):
+            batch_end = min(batch_start + SYM_BATCH, total_syms)
+            batch_syms = all_symbols[batch_start:batch_end]
 
-        # 只读取本批 symbol 的数据
-        lf_batch = scan_daily_parquet(daily_glob, cast_options=_cast)
-        lf_batch = lf_batch.filter(pl.col("symbol").is_in(batch_syms))
-        raw = lf_batch.sort(["symbol", "date"]).collect(streaming=True)
+            # 只读取本批 symbol 的数据
+            lf_batch = scan_daily_parquet(daily_files, cast_options=_cast)
+            lf_batch = lf_batch.filter(pl.col("symbol").is_in(batch_syms))
+            raw = lf_batch.sort(["symbol", "date"]).collect(streaming=True)
 
-        if raw.is_empty():
-            continue
+            if raw.is_empty():
+                continue
 
-        # 本批的 factors / instruments
-        batch_factors = (
-            factors.filter(pl.col("symbol").is_in(batch_syms))
-            if not factors.is_empty() else factors
-        )
-        batch_inst = (
-            inst_use.filter(pl.col("symbol").is_in(batch_syms))
-            if not inst_use.is_empty() else inst_use
-        )
-        batch_shares = (
-            historical_shares.filter(pl.col("symbol").is_in(batch_syms))
-            if not historical_shares.is_empty() else historical_shares
-        )
+            # 本批的 factors / instruments
+            batch_factors = (
+                factors.filter(pl.col("symbol").is_in(batch_syms))
+                if not factors.is_empty() else factors
+            )
+            batch_inst = (
+                inst_use.filter(pl.col("symbol").is_in(batch_syms))
+                if not inst_use.is_empty() else inst_use
+            )
+            batch_shares = (
+                historical_shares.filter(pl.col("symbol").is_in(batch_syms))
+                if not historical_shares.is_empty() else historical_shares
+            )
 
-        # 计算
-        enriched = compute_enriched(
-            raw,
-            factors=batch_factors,
-            instruments=batch_inst,
-            historical_shares=batch_shares,
-        )
+            # 计算
+            enriched = compute_enriched(
+                raw,
+                factors=batch_factors,
+                instruments=batch_inst,
+                historical_shares=batch_shares,
+            )
 
-        if not enriched.is_empty():
-            if symbols:
-                # 局部模式: 直接按日期合并写入
-                for date_df in enriched.partition_by("date"):
-                    dt = date_df["date"][0]
-                    ds = dt.isoformat() if hasattr(dt, "isoformat") else str(dt)
-                    out = base / f"date={ds}" / "part.parquet"
+            if not enriched.is_empty():
+                if symbols:
+                    # 局部模式: 直接按日期合并写入
+                    for date_df in enriched.partition_by("date"):
+                        dt = date_df["date"][0]
+                        ds = dt.isoformat() if hasattr(dt, "isoformat") else str(dt)
+                        out = base / f"date={ds}" / "part.parquet"
+                        out.parent.mkdir(parents=True, exist_ok=True)
+                        date_df_storage = _select_storage_cols(date_df)
+                        if out.exists():
+                            existing = pl.read_parquet(out)
+                            existing = existing.filter(~pl.col("symbol").is_in(batch_syms))
+                            date_df_storage = pl.concat([existing, date_df_storage], how="diagonal_relaxed")
+                        date_df_storage = date_df_storage.sort(["symbol"])
+                        publication.write_parquet(date_df_storage, out)
+                        written += date_df_storage.height
+                else:
+                    # 全量模式: 写单批暂存文件 (按 date,symbol 排序 →
+                    # 合并期 parquet 行组统计可按日期裁剪), 随即释放本批内存
+                    out = staging_dir / f"batch-{batch_start // SYM_BATCH:04d}.parquet"
+                    _select_storage_cols(enriched).sort(["date", "symbol"]).write_parquet(out)
+                    staging_files.append(str(out))
+                    written += enriched.height
+
+            del raw, enriched, batch_factors, batch_inst, batch_shares
+            gc.collect()
+
+            logger.info("symbol 批次 %d/%d (%s ~ %s), 已处理 %d 行",
+                         batch_start // SYM_BATCH + 1,
+                         total_batches,
+                         batch_syms[0], batch_syms[-1], written)
+
+            # 通知进度
+            if on_batch_done:
+                on_batch_done(batch_start // SYM_BATCH + 1, total_batches)
+
+        # 全量模式: 日期覆盖校验 → 按日期分块流式合并 → 逐分区原子替换
+        if not symbols and staging_files:
+            existing_dates = {
+                p.name.removeprefix("date=")
+                for p in base.glob("date=*")
+                if p.is_dir()
+            }
+            unique_dates = sorted(
+                scan_enriched_parquet(staging_files).select("date").unique()
+                .collect()["date"].to_list()
+            )
+            rebuilt_dates = {
+                ds.isoformat() if hasattr(ds, "isoformat") else str(ds)
+                for ds in unique_dates
+            }
+            missing_dates = existing_dates - rebuilt_dates
+            if missing_dates:
+                sample = ", ".join(sorted(missing_dates)[:5])
+                raise RuntimeError(f"全量重建结果缺少已有日期分区,拒绝覆盖: {sample}")
+
+            base.mkdir(parents=True, exist_ok=True)
+
+            chunk = max(1, -(-len(unique_dates) // _MERGE_DATE_CHUNKS))
+            for ci in range(0, len(unique_dates), chunk):
+                lo = unique_dates[ci]
+                hi = unique_dates[min(ci + chunk, len(unique_dates)) - 1]
+                block = (
+                    scan_enriched_parquet(staging_files)
+                    .filter((pl.col("date") >= lo) & (pl.col("date") <= hi))
+                    .sort(["date", "symbol"])
+                    .collect(streaming=True)
+                )
+                for date_df in block.partition_by("date"):
+                    ds = date_df["date"][0]
+                    ds_str = ds.isoformat() if hasattr(ds, "isoformat") else str(ds)
+                    out = base / f"date={ds_str}" / "part.parquet"
                     out.parent.mkdir(parents=True, exist_ok=True)
-                    date_df_storage = _select_storage_cols(date_df)
-                    if out.exists():
-                        existing = pl.read_parquet(out)
-                        existing = existing.filter(~pl.col("symbol").is_in(batch_syms))
-                        date_df_storage = pl.concat([existing, date_df_storage], how="diagonal_relaxed")
-                    date_df_storage = date_df_storage.sort(["symbol"])
-                    publication.write_parquet(date_df_storage, out)
-                    written += date_df_storage.height
-            else:
-                # 全量模式: 缓冲到 date_buffers, 最后一次性写入
-                for date_df in enriched.partition_by("date"):
-                    dt = date_df["date"][0]
-                    ds = dt.isoformat() if hasattr(dt, "isoformat") else str(dt)
-                    date_buffers[ds].append(_select_storage_cols(date_df).sort(["symbol"]))
-                    written += date_df.height
-
-        del raw, enriched, batch_factors, batch_inst, batch_shares
-        gc.collect()
-
-        logger.info("symbol 批次 %d/%d (%s ~ %s), 已处理 %d 行",
-                     batch_start // SYM_BATCH + 1,
-                     total_batches,
-                     batch_syms[0], batch_syms[-1], written)
-
-        # 通知进度
-        if on_batch_done:
-            on_batch_done(batch_start // SYM_BATCH + 1, total_batches)
-
-    # 全量模式: 按日期分区写入
-    if not symbols and date_buffers:
-        existing_dates = {
-            p.name.removeprefix("date=")
-            for p in base.glob("date=*")
-            if p.is_dir()
-        }
-        rebuilt_dates = set(date_buffers)
-        missing_dates = existing_dates - rebuilt_dates
-        if missing_dates:
-            sample = ", ".join(sorted(missing_dates)[:5])
-            raise RuntimeError(f"全量重建结果缺少已有日期分区,拒绝覆盖: {sample}")
-
-        base.mkdir(parents=True, exist_ok=True)
-
-        for ds, dfs in date_buffers.items():
-            out = base / f"date={ds}" / "part.parquet"
-            out.parent.mkdir(parents=True, exist_ok=True)
-            merged = pl.concat(dfs, how="diagonal_relaxed").sort(["symbol"])
-            publication.write_parquet(merged, out)
-
-        date_buffers.clear()
-        gc.collect()
+                    publication.write_parquet(date_df.sort(["symbol"]), out)
+            gc.collect()
+            logger.info("全量暂存合并完成: %d 个日期分区", len(unique_dates))
+    finally:
+        # 无论成功/失败/取消都清掉本次暂存 (历史残留由 _sweep_stale_staging 兜底)
+        if staging_dir is not None:
+            shutil.rmtree(staging_dir, ignore_errors=True)
 
     publication.commit()
     t_done = _t.perf_counter()
diff --git a/backend/app/tickflow/repository.py b/backend/app/tickflow/repository.py
index f84cb55..33b0878 100644
--- a/backend/app/tickflow/repository.py
+++ b/backend/app/tickflow/repository.py
@@ -582,7 +582,7 @@ class KlineRepository:
             # 300 日历天 ≈ 210 交易日, 覆盖 filter_history 最大 lookback(90) + warmup(60)
             try:
                 from datetime import timedelta
-                from app.indicators.pipeline import compute_indicators, compute_signals, compute_limit_signals
+                from app.indicators.pipeline import compute_enriched_history_window
                 start_full = latest - timedelta(days=300)
                 read_cols = [c for c in ["symbol", "date", "open", "high", "low", "close",
                                          "volume", "amount", "raw_close", "raw_high", "raw_low"]
@@ -600,28 +600,22 @@ class KlineRepository:
                 if not df_hist.is_empty():
                     instruments = self._instruments_cache if self._instruments_cache is not None else pl.DataFrame()
 
+                    # 分批执行 指标→偏离→信号→涨跌停 (与整帧顺序等价, 各步骤均
+                    # over("symbol") 分组), 峰值内存与标的总量解耦 (#208)
                     step = time.perf_counter()
-                    logger.info("enriched refresh step start: compute indicators")
-                    df_full = compute_indicators(df_hist)
-                    logger.info("enriched refresh step done: compute indicators rows=%d (%.2fs)", len(df_full), time.perf_counter() - step)
-
-                    # 异动偏离列 (deviate_Nd = 个股动量 - 基准指数动量), 运行时附着
-                    from app.indicators.pipeline import attach_deviation_columns
-                    df_full = attach_deviation_columns(df_full, self.store.data_dir)
-
-                    step = time.perf_counter()
-                    logger.info("enriched refresh step start: compute signals")
-                    df_full = compute_signals(df_full)
-                    logger.info("enriched refresh step done: compute signals (%.2fs)", time.perf_counter() - step)
-                    if instruments is not None and not instruments.is_empty():
-                        step = time.perf_counter()
-                        logger.info("enriched refresh step start: compute limit signals")
-                        df_full = compute_limit_signals(
-                            df_full,
-                            instruments,
-                            historical_shares=self.get_historical_shares(),
-                        )
-                        logger.info("enriched refresh step done: compute limit signals (%.2fs)", time.perf_counter() - step)
+                    logger.info("enriched refresh step start: compute window (batched)")
+                    df_full = compute_enriched_history_window(
+                        df_hist,
+                        self.store.data_dir,
+                        instruments=instruments,
+                        historical_shares=(
+                            self.get_historical_shares()
+                            if instruments is not None and not instruments.is_empty()
+                            else None
+                        ),
+                    )
+                    logger.info("enriched refresh step done: compute window rows=%d (%.2fs)",
+                                len(df_full), time.perf_counter() - step)
 
                     # JOIN instruments 到完整历史 (filter_history/basic_filter 需要 name/股本等列)
                     if instruments is not None and not instruments.is_empty():
diff --git a/backend/tests/test_enriched_full_rebuild.py b/backend/tests/test_enriched_full_rebuild.py
index 913a15d..836b2c4 100644
--- a/backend/tests/test_enriched_full_rebuild.py
+++ b/backend/tests/test_enriched_full_rebuild.py
@@ -78,3 +78,120 @@ def test_full_rebuild_rejects_missing_existing_dates_before_writing(tmp_path, mo
         tmp_path / "kline_daily_enriched" / "date=2026-07-15" / "part.parquet"
     )
     assert existing["close"].to_list() == [1.0]
+
+
+# ================================================================
+# 流式暂存发布 + 自适应批次 (#208/#174)
+# ================================================================
+
+def _write_daily_multi(data_dir, symbols: list[str], dates: list[str]) -> None:
+    for ds in dates:
+        rows = {
+            "symbol": symbols,
+            "date": [date.fromisoformat(ds)] * len(symbols),
+            "open": [10.0] * len(symbols),
+            "high": [11.0] * len(symbols),
+            "low": [9.5] * len(symbols),
+            "close": [10.5] * len(symbols),
+            "volume": [100.0] * len(symbols),
+            "amount": [1000.0] * len(symbols),
+        }
+        out = data_dir / "kline_daily" / f"date={ds}" / "part.parquet"
+        out.parent.mkdir(parents=True, exist_ok=True)
+        pl.DataFrame(rows).write_parquet(out)
+
+
+def test_full_rebuild_streaming_output_matches_direct_compute(tmp_path, monkeypatch):
+    """多批流式暂存 + 分块合并的输出与整帧直算完全一致 (#208)。"""
+    symbols = [f"{600000 + i}.SH" for i in range(4)]
+    dates = [f"2026-07-{d:02d}" for d in range(1, 6)]
+    _write_daily_multi(tmp_path, symbols, dates)
+    # 强制 2 只/批 → 2 批暂存 + 合并, 覆盖流式路径
+    monkeypatch.setattr(pipeline, "_adaptive_sym_batch", lambda default, rows: 2)
+
+    written = pipeline.run_pipeline(data_dir=tmp_path)
+    assert written == len(symbols) * len(dates)
+
+    from app.parquet import scan_daily_parquet
+    raw = (scan_daily_parquet(str(tmp_path / "kline_daily" / "**" / "*.parquet"))
+           .sort(["symbol", "date"]).collect())
+    expected = pipeline._select_storage_cols(pipeline.compute_enriched(raw)).sort(["symbol", "date"])
+
+    got = pl.read_parquet(str(tmp_path / "kline_daily_enriched" / "**" / "*.parquet"))
+    got_cols = [c for c in expected.columns if c in got.columns]
+    assert got.select(got_cols).sort(["symbol", "date"]).equals(expected.select(got_cols))
+
+
+def test_full_rebuild_cleans_staging_and_keeps_it_outside_globs(tmp_path, monkeypatch):
+    """重建完成后暂存目录被清理, 业务 glob 不会扫到暂存文件 (#208)。"""
+    _write_daily(tmp_path, "2026-07-14", 14.0)
+    _write_daily(tmp_path, "2026-07-15", 15.0)
+    monkeypatch.setattr(pipeline, "compute_enriched", _fake_compute_enriched)
+
+    pipeline.run_pipeline(data_dir=tmp_path)
+
+    staging_root = tmp_path / ".staging" / "enriched_rebuild"
+    assert not staging_root.exists() or not any(staging_root.iterdir())
+    # 暂存位于 enriched 树外: enriched glob 只见 date=* 分区
+    parts = list((tmp_path / "kline_daily_enriched").glob("date=*"))
+    assert sorted(p.name for p in parts) == ["date=2026-07-14", "date=2026-07-15"]
+
+
+def test_stale_staging_swept_on_full_rebuild(tmp_path, monkeypatch):
+    """崩溃/取消残留的暂存目录按 mtime 被清扫 (#208)。"""
+    import os
+    import time as _time
+    stale = tmp_path / ".staging" / "enriched_rebuild" / "dead-run"
+    stale.mkdir(parents=True)
+    junk = stale / "batch-0000.parquet"
+    junk.write_bytes(b"junk")
+    old = _time.time() - 2 * 24 * 3600
+    os.utime(stale, (old, old))
+
+    _write_daily(tmp_path, "2026-07-14", 14.0)
+    monkeypatch.setattr(pipeline, "compute_enriched", _fake_compute_enriched)
+    pipeline.run_pipeline(data_dir=tmp_path)
+
+    assert not stale.exists()
+
+
+def test_adaptive_sym_batch_shrinks_only_on_small_ram(monkeypatch):
+    """小内存: 按目标行数收缩; 大内存: 保持用户设置 (#208)。"""
+    monkeypatch.setattr(pipeline, "_total_ram_bytes", lambda: 2 * 1024 ** 3)
+    got = pipeline._adaptive_sym_batch(1000, 1500)
+    assert got == max(pipeline._BATCH_MIN_SYMBOLS, min(1000, 150_000 // 1500))
+    # 行数极少时不低于下限
+    assert pipeline._adaptive_sym_batch(1000, 1) == 1000
+
+    monkeypatch.setattr(pipeline, "_total_ram_bytes", lambda: 16 * 1024 ** 3)
+    assert pipeline._adaptive_sym_batch(1000, 1500) == 1000
+
+
+def test_history_window_batched_equals_direct(tmp_path):
+    """刷新窗口分批计算与整帧顺序执行等价 (#208)。"""
+    import numpy as np
+    rng = np.random.default_rng(11)
+    n_syms, n_days = 6, 30
+    close = 10.0 * np.cumprod(1 + rng.normal(0, 0.02, (n_syms, n_days)), axis=1)
+    df_hist = pl.DataFrame({
+        "symbol": np.repeat([f"{600000 + i}.SH" for i in range(n_syms)], n_days),
+        "_day": np.tile(np.arange(n_days), n_syms),
+        "open": (close * 0.99).reshape(-1),
+        "high": (close * 1.01).reshape(-1),
+        "low": (close * 0.98).reshape(-1),
+        "close": close.reshape(-1),
+        "volume": rng.integers(1000, 9000, n_syms * n_days).astype(float),
+        "amount": rng.integers(1, 99, n_syms * n_days).astype(float),
+    }).with_columns(
+        (pl.lit(date(2026, 6, 1)) + pl.duration(days=pl.col("_day"))).alias("date")
+    ).drop("_day")
+
+    direct = pipeline.compute_signals(
+        pipeline.attach_deviation_columns(
+            pipeline.compute_indicators(df_hist.clone()), tmp_path
+        )
+    )
+    batched = pipeline.compute_enriched_history_window(
+        df_hist.clone(), tmp_path, sym_batch=2
+    )
+    assert batched.sort(["symbol", "date"]).equals(direct.sort(["symbol", "date"]))